Biology subjects

Kazi, A.

Publications and source records attributed to Kazi, A..

5 recordsLinked to original sources

bioRxiv2026

Tumor-hepatocyte crosstalk drives a hepatic lactate-TGF-β axis of CD8⁺ T cell exhaustion and immunotherapy resistance in small-cell lung cancer liver metastases

PurposeLiver metastases confer poor outcomes and attenuate the benefit of immunotherapy across solid tumors. This study investigated how the hepatic metastatic niche promotes CD8 T cell dysfunction and immunotherapy resistance in small-cell lung cancer (SCLC). Experimental DesignClinical outcomes and tumor gene expression were integrated with multi-region single-cell RNA sequencing of T cells from rapid-autopsy SCLC metastases, together with spatial transcriptomics. SCLC-hepatocyte conditioned-media models were combined with stable-isotope tracing, mass spectrometry, functional and metabolic assays, and ChIP-qPCR to define mechanisms of CD8 T cell suppression. ResultsLiver metastases were associated with inferior survival and reduced benefit from immune checkpoint blockade. Multi-region single-cell analysis showed that CD8 T cells from liver metastases exhibited an exhaustion-associated state enriched for hypoxia, lactate, and TGF-{beta} programs. SCLC-hepatocyte crosstalk generated a lactate- and TGF-{beta}-rich microenvironment that reduced CD8 T cell effector function, proximal T cell receptor signaling, glycolytic fitness, viability, and proliferation. Stable-isotope tracing demonstrated transfer and accumulation of co-culture-derived lactate in recipient CD8 T cells, with limited entry into downstream pyruvate-linked pathways. Lactate accumulation was accompanied by increased H3K18 lactylation at the PDCD1, LAG3, and TGFB1 regulatory loci. In parallel, SCLC-hepatocyte crosstalk increased paracrine TGF-{beta} and activated canonical SMAD2 signaling in CD8 T cells. TGF-{beta} receptor inhibition restored CD8 T cell proliferation. In the phase III IMpower133 cohort, a combined lactate-TGF-{beta} transcriptional program was associated with inferior survival, most strongly in patients with liver metastases. ConclusionsTumor-hepatocyte crosstalk generates convergent lactate and TGF-{beta} signals that drive CD8 T cell dysfunction in liver metastases. This hepatic immune-metabolic circuit provides a potential mechanism for immunotherapy resistance and supports therapeutic strategies targeting TGF-{beta} signaling in liver-metastatic SCLC. Translational RelevancePatients with SCLC liver metastases have poor outcomes and derive limited benefit from immune checkpoint blockade, but actionable mechanisms of hepatic immune resistance remain undefined. We identify an immune-metabolic circuit in which SCLC-hepatocyte crosstalk generate lactate and TGF-{beta} signals that converge on CD8 T cells. Stable-isotope tracing demonstrates the transfer and accumulation of tumor-hepatocyte-derived lactate in recipient T cells, which causes H3K18 lactylation at exhaustion- and TGFB1-associated loci. In parallel, paracrine TGF-{beta} activates canonical SMAD signaling and reinforces proliferative dysfunction. TGF-{beta} receptor inhibition restores CD8 T cell proliferation. In the phase III IMpower133 cohort, a combined lactate-TGF-{beta} program is associated with inferior survival, particularly among patients with liver metastases. These findings provide a mechanistic and biomarker framework for testing TGF-{beta}-directed strategies in liver-metastatic SCLC, a population with substantial unmet clinical need.

cancer biology↗

bioRxiv2026

Hepatobiliary Progenitor-like Reprogramming in Liver Metastases

Metastatic progression requires cancer cells to adapt to the unique constraints of distant organ microenvironments, yet the mechanisms that drive organ-specific adaptations remain poorly understood. Here, we show that the liver actively rewrites metastatic cancer cell identity, driving tumor cells toward a hepatobiliary progenitor-like state. Through integrated transcriptomic, proteomic, metabolomic, and epigenomic analyses of patient-derived rapid-autopsy samples and experimental models, we identify this state as selectively enriched in liver metastases. It is characterized by co-activation of hepatic and biliary/progenitor regulators HNF4A and SOX9 and is observed across multiple epithelial cancers, indicating a conserved response to the hepatic niche. Mechanistically, hepatocyte-derived TGF-{beta} and hypoxia converge to activate a HIF-1-ACLY axis, increasing nuclear acetyl-coenzyme A availability and histone acetylation at hepatic lineage regulatory elements to drive hepatobiliary reprogramming. This coordinated niche-response program can be captured transcriptionally and is associated with inferior overall survival. Disruption of this pathway suppresses hepatic reprogramming and impairs liver metastatic fitness. These findings identify the liver as an active determinant of metastatic cell fate, linking microenvironmental signaling to metabolic and chromatin remodeling programs that enable lineage plasticity. More broadly, they reveal organ-specific reprogramming as a fundamental principle of metastasis and a therapeutic vulnerability in liver metastases.

cancer biology↗

bioRxiv2025

Multimodal Connectivity-based Cortical Segmentation with Graph Neural Networks

Due to the significant amount of time and expertise needed for manual segmentation of the brain cortex from magnetic resonance imaging (MRI) data, there is a substantial need for efficient and accurate algorithms to replace the need for human involvement. In this work, we explore the capabilities of Graph Neural Networks (GNNs) to segment the brain surface based on structural brain connectivity. We train three different GNN architectures, the Graph Convolutional Network (GCN), the Graph Attention Network (GAT), and the Graph U-Net, and evaluate their performances when trained on silver-standard cortical region labels created by FreeSurfer. We take a multimodal approach to brain segmentation by examining the influence of the structural connectivity values inferred from diffusion MRI (dMRI) in addition to using values from structural MRI (sMRI). Our results demonstrate the utility of GNN models, particularly the GAT architecture, which achieved Dice scores competitive to those reported in the literature with non-graph methods. Additionally, structural connectivity derived from dMRI revealed significant value in improving automatic segmentation, as models trained on combined attributes from dMRI and sMRI outperformed those trained only on sMRI. Finally, we compared the GNN-based and the FreeSurfer segmentations in their ability to predict demographic/clinical data, where neither of the two approaches was statistically significantly superior to the other.

neuroscience↗

bioRxiv2025

Structural Connectome Analysis using a Graph-based Deep Model for Age and Dementia Prediction

We address the prediction of non-imaging variables based on structural brain connectivity derived from diffusion magnetic resonance images, using graph-based machine learning. We predict age and the mini-mental state examination (MMSE) score as examples of a demographic and a clinical variable. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity graph as input and processes the data separately through a parallel GCN mechanism with multiple branches. The novelty of our work lies in the model architecture, especially the Connectivity Attention Block, which learns an embedding representation of brain graphs while providing graph-level attention. We show experiments on publicly available datasets of PREVENT-AD and OASIS3. We validate our model by comparing it to existing methods and via ablations. This quantifies the degree to which the connectome varies depending on the task, which is important for improving our understanding of health and disease across the population. The proposed model generally demonstrates higher performance especially for age prediction compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning.

bioinformatics↗

bioRxiv2019

The Vitamin D Receptor (VDR) Regulates Mitochondrial Function in C2C12 Myoblasts

Vitamin D deficiency has been linked to a reduction in skeletal muscle function and oxidative capacity, however, the mechanistic basis of these impairments are poorly understood. The biological actions of vitamin D are carried out via the binding of 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) to the vitamin D receptor (VDR). Recent evidence has linked 1,25(OH)2D3 to the regulation of skeletal muscle mitochondrial function in vitro, however, little is known with regard to the role of the VDR in this process. To examine the regulatory role of the VDR in skeletal muscle mitochondrial function, we utilised lentiviral mediated shRNA silencing of the VDR in C2C12 myoblasts (VDR-KD) and examined mitochondrial respiration and protein content compared to shRNA scrambled control. VDR protein content was reduced by ~95% in myoblasts and myotubes (P < 0.001). VDR-KD myoblasts displayed a 30%, 30% and 36% reduction in basal, coupled and maximal respiration respectively (P < 0.05). This phenotype was maintained in VDR-KD myotubes, displaying a 34%, 33% and 48% reduction in basal, coupled and maximal respiration (P < 0.05). Furthermore, ATP production derived from oxidative phosphorylation (ATPox) was reduced by 20% suggesting intrinsic impairments within the mitochondria following VDR-KD. However, despite the observed functional decrements, mitochondrial protein content as well as markers of fusion and fission were unchanged. In summary, we highlight a direct role for the VDR in regulating skeletal muscle mitochondrial respiration in vitro, providing a potential mechanism as to how vitamin D deficiency might impact upon skeletal muscle oxidative capacity.

physiology↗